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基于大型语言模型的突变在遗传改进中的突变
Alexander E I Brownlee1, James Callan2, Karine Even-Mendoza3
1University of Stirling, Scotland, UK.
概括
大型语言模型 (LLM) 在基于搜索的软件工程 (SBSE) 中作为突变运营者表现有前途,用于基因改进 (GI). 与传统方法相比,LLM产生较少,但更有效的,通过测试的代码编辑.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 在软件工程中越来越多地使用.
- 将LLMs与基于搜索的软件工程 (SBSE) 结合起来,用于基因改进 (GI) 仍未得到充分探索.
研究的目的:
- 评估LLM作为SBSE中的GI的突变运营商.
- 评估不同LLM,提示和软件项目的LLM生成代码编辑的有效性.
主要方法:
- 在五个现实世界软件项目中使用了三个LLM和三个提示类型.
- 采用随机抽样和本地搜索进行编辑生成.
- 对LLM生成的代码编辑失败进行了定性分析.
主要成果:
- 与传统的声明GI编辑相比,LLM产生了更少的独特编辑.
- 在LLM生成的编辑中,编译和通过测试的频率更高 (OpenAI:77%).
- 在识别运行时间改进方面,OpenAI和Mistral LLMs表现相似;更简单的提示更有效.
结论:
- 对于SBSE,LLM为GI中的传统突变运营商提供了一个可行的替代方案.
- 虽然LLM生成的编辑次数较少,但它们在生成功能代码时的成功率更高.
- 常见的故障模式包括格式不一致,无效语法和拒绝生成解决方案,需要进一步研究提示工程和模型改进.
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